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<title>Machine Learning Research Software</title>

<h1>Machine Learning Research Software</h1> 
<br> 
We have compiled a group of Common Lisp files for various inductive
classification algorithms.  These algorithms are intended for research
purposes and all use the same basic data format and interface.  Also
included is automatic testing software for running learning curves
that compare multiple systems and utilities for plotting and
statistically evaluating the results.  <p>

This software is all available via 
<!WA0><a href="ftp://ftp.cs.utexas.edu/pub/mooney/ml-progs">anonymous ftp</a>. <p>


Current Algorithms: <p>

<ol>
<li> <b>AQ</b> - An early DNF learner.
<li> <b>Backprop</b> - The standard multi-layer neural-net learning method.
<li> <b>Bayes Indp</b> - A simple naive or "idiot's" Bayesian classifier.
<li> <b>Cobweb</b> - A probabilistic clustering system.
<li> <b>FOIL</b> - A first-order Horn-clause learner (Prolog and Lisp versions).
<li> <b>ID3</b> - A decision tree learner with a number of features.
<li> <b>KNN</b> - A k nearest neighbor (instance-based) algorithm.
<li> <b>Perceptron</b> - An early one-layer neural-net algorithm.
<li> <b>PFOIL</b> - A propositional version of FOIL for learning DNF.
<li> <b>PFOIL-CNF</b> - A propositional version of FOIL for learning CNF.
</ol>


Some sample data sets included are "dna-standard.lisp" and
"labor-neg.lisp".  The file "data-utilties.lisp" should be loaded
before any other code.  Comments at the beginning of "universal-tester.lisp" 
help define the data format and interface standards used.  The file 
"data-utilities.lisp" also includes a function for converting a data file 
suitable for Quinlan's C4.5 to a format usable by these algorithms. <p>


<hr>
<address><!WA1><a href="http://www.cs.utexas.edu/users/estlin/">estlin@cs.utexas.edu</a></address>
